Titlebook: Artificial Intelligence and Industrial Applications; Smart Operation Mana Tawfik Masrour,Anass Cherrafi,Ibtissam El Hassani Conference proc

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Prediction of Robot Localization States Using Hidden Markov Models,ate the map of an abandoned coal mine. The prediction of the robot localization with precision still a big problem, for this reason, our study consists of a robot, which can move within an area of 9 squares. This robot is equipped with the sensing system, which detects obstacles in four directions:

令人發(fā)膩

A New Approach for Multi-agent Reinforcement Learning,-error, the agent has to learn to maximize its total accumulated reward. Several algorithms and techniques were developed for a single agent reinforcement learning. Our purpose is to benefit from all done work in reinforcement learning of an agent and extend it to multi-agent system. we have propose

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2194-5357 eedings of the Artificial Intelligence & Industrial Applicat.This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA’2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Moroc

北極人

Criticism of the Twelve Masters,ing algorithms, as an alternative to classic methods. Indeed, the classical approaches are based on a pre-established policy of vehicle movement rules, while our method deduces the movement rules based on trial/error reinforcement learning approach, and effectively gives good results in relatively small moving areas and a limited number of agents.

Harridan

On the Influence of Confucianism,s of a robot, which can move within an area of 9 squares. This robot is equipped with the sensing system, which detects obstacles in four directions: north, south, east and west. The sensors have an error rate of e?=?25%. The objective of this work is the prediction of the robot localization using Hidden Markov Model.

很是迷惑

Deep Learning Approach for Automated Guided Vehicle System,ing algorithms, as an alternative to classic methods. Indeed, the classical approaches are based on a pre-established policy of vehicle movement rules, while our method deduces the movement rules based on trial/error reinforcement learning approach, and effectively gives good results in relatively small moving areas and a limited number of agents.